Skip to main content
Log in

Proteomic patterns as a diagnostic tool for early-stage cancer

A review of its progress to a clinically relevant tool

  • Review Article
  • Published:
Molecular Diagnosis Aims and scope Submit manuscript

Abstract

The pace of development in novel technologies that promise improvements in the early diagnosis of disease is truly impressive. One such technology at the forefront of this revolution is mass spectrometry. New capabilities in mass spectrometry have provided the means for the development of proteomics, and the race is on to find innovative ways to apply this powerful technology to solving the problems faced in clinical medicine. One area that has garnered much attention over the past few years is the use of mass spectral patterns for cancer diagnostics.

The use of these so-called ‘proteomic patterns’ for disease diagnosis relies fundamentally on the pattern of signals observed within a mass spectrum rather than the more conventional identification and quantitation of a biomarker such as in the case of cancer antigen-125-or prostate-specific antigen. The inherent throughput of proteomic pattern technology enables the analysis of hundreds of clinical samples per day. Currently, there are two primary means by which proteomic patterns can be acquired, surface-enhanced laser desorption/ionization (SELDI) and an electrospray ionization (ESI) method that has been popularized under the name, OvaCheck™. In this review, an historical perspective on the development of proteomic patterns for the diagnosis of early-stage cancers is described. In addition, a critical assessment of the overall technology is presented with an emphasis on the steps required to enable proteomic pattern analysis to become a viable clinical tool for diagnosing early-stage cancers.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Table I

Similar content being viewed by others

References

  1. Chan KC, Lucas DA, et al. Analysis of the human serum proteome. Clin Prot 2004; 1: 101–226

    Article  Google Scholar 

  2. Pieper R, Su Q, Gatlin CL, et al. Multi-component immunoaffinity subtraction chromatography: an innovative step towards a comprehensive survey of the human plasma proteome. Proteomics 2003; 3: 422–32

    Article  PubMed  CAS  Google Scholar 

  3. Jona G, Snyder M. Recent developments in analytical and functional protein microarrays. Curr Opin Mol Ther 2003; 5: 271–7

    PubMed  CAS  Google Scholar 

  4. Klein E, Klein JB, Thongboonkerd V. Two-dimensional gel electrophoresis: a fundamental tool for expression proteomics studies. Contrib Nephrol 2004; 141: 25–39

    Article  PubMed  CAS  Google Scholar 

  5. Gygi SP, Rist B, Gerber SA, et al. Quantitative analysis of complex protein mixtures using isotope-coded affinity tags. Nat Biotechnol 1999; 17: 994–9

    Article  PubMed  CAS  Google Scholar 

  6. Johnson MD, Yu LR, Conrads TP, et al. Proteome analysis of DNA damage-induced neuronal death using high throughput mass spectrometry. J Biol Chem 2004; 279(25): 26685–97

    Article  PubMed  CAS  Google Scholar 

  7. Yanagisawa K, Shyr Y, Xu BJ, et al. Proteomic patterns of tumor subsets in non-small-cell lung cancer. Lancet 2003; 362: 433–9

    Article  PubMed  CAS  Google Scholar 

  8. Issaq HJ, Conrads TP, Prieto DA, et al. SELDI-TOF MS for diagnostic proteomics. Anal Chem 2003; 75: 148A-55A

    Article  Google Scholar 

  9. Wright Jr GL. SELDI proteinchip MS: a platform for biomarker discovery and cancer diagnosis. Expert Rev Mol Diagn 2002; 2: 549–63

    Article  PubMed  CAS  Google Scholar 

  10. Conrads TP, Zhou M, Petricoin III EF, et al. Cancer diagnosis using proteomic patterns. Expert Rev Mol Diagn. 2003 Jul; 3(4): 411–20

    Article  PubMed  CAS  Google Scholar 

  11. Hutchens TW, Yip TT. New desorption strategies for the mass spectrometric analysis of macromolecules. Rapid Commun Mass Spectrom 1993; 7: 576–80

    Article  CAS  Google Scholar 

  12. Petricoin EF, Ardekani AM, Hitt BA, et al. Use of proteomic patterns in serum to identify ovarian cancer. Lancet 2002; 359: 572–7

    Article  PubMed  CAS  Google Scholar 

  13. Holland JH, editor. Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. Cambridge (MA): MIT Press, 1994

    Google Scholar 

  14. Kohonen Y. Self-organizing formation of topologically correct feature maps. Biol Cybern 1982; 43: 59–69

    Article  Google Scholar 

  15. Kohonen Y. The self-organized map. Proc Inst Electrical Electronics Eng 1990; 78: 1464–80

    Google Scholar 

  16. Jacobs IJ, Skates SJ, MacDonald N, et al. Screening for ovarian cancer: a pilot randomised controlled trial. Lancet 1999; 353: 1207–10

    Article  PubMed  CAS  Google Scholar 

  17. National Cancer Institute. Surveillance, epidemiology, and end results [oline]. Available from URL: http://seer.cancer.gov [Accessed 2004 Sep 10]

  18. Pusztai L, Gregory BW, Baggerly KA, et al. Pharmacoproteomic analysis of prechemotherapy and postchemotherapy plasma samples from patients receiving neoadjuvant or adjuvant chemotherapy for breast carcinoma. Cancer 2004; 100: 1814–22

    Article  PubMed  CAS  Google Scholar 

  19. Wagner M, Naik DN, Pothen A, et al. Computational protein biomarker prediction: a case study for prostate cancer. BMC Bioinform 2004; 5: 26

    Article  Google Scholar 

  20. Zhukov TA, Johanson RA, Cantor AB, et al. Discovery of distinct protein profiles specific for lung tumors and pre-malignant lung lesions by SELDI mass spectrometry. Lung Cancer 2003; 40: 267–79

    Article  PubMed  Google Scholar 

  21. Koopmann J, Zhang Z, White N, et al. Serum diagnosis of pancreatic adenocarcinoma using surface-enhanced laser desorption and ionization mass spectrometry. Clin Cancer Res 2004; 10: 860–8

    Article  PubMed  CAS  Google Scholar 

  22. Wadsworth JT, Somers KD, Cazares LH, et al. Serum protein profiles to identify head and neck cancer. Clin Cancer Res 2004; 10: 1625–32

    Article  PubMed  CAS  Google Scholar 

  23. Xiao Z, Luke BT, Izmirlian G, et al. Serum proteomic profiles suggest celecoxibmodulated targets and response predictors. Cancer Res 2004; 64: 2904–9

    Article  PubMed  CAS  Google Scholar 

  24. Conrads TP, Fusaro VA, Ross S, et al. High-resolution serum proteomic features for ovarian cancer detection. Endocr Relat Cancer 2004; 11: 163–78

    Article  PubMed  CAS  Google Scholar 

  25. O’Rourke J, Mahon SM. A comprehensive look at the early detection of ovarian cancer. Clin J Oncol Nurs 2003; 7: 41–7

    Article  PubMed  Google Scholar 

  26. Ozols RF, Rubin SC, Thomas GM, et al. Epithelial ovarian cancer. In: Hoskins WJ, Perez CA, Young RC, editors. Principles and practice of gynecologic oncology. Philadelphia (PA): Lippincott Williams and Wilkins, 2000: 981–1058

    Google Scholar 

  27. Kainz C. Early detection and preoperative diagnosis of ovarian carcinoma. Wien Med Wochenschr 1996; 146: 2–7

    PubMed  CAS  Google Scholar 

  28. Eltabbakh GH, Belinson JL, Kennedy AW, et al. Serum CA-125 measurements > 65 U/mL. Clinical value. J Reprod Med 1997; 42: 617–24

    CAS  Google Scholar 

  29. Progress from unraveling proteins [online]. Posted Apr 19, 2004. Available from URL: http://www.correlogic.com/inquirer.pdf [Accessed 2004 Aug 30]

  30. New Jersey oncologist says ovarian cancer test may catch disease early [online]. Posted Apr 14, 2004. Available from URL: http://www.miami.com/mld/miamiherald/business/national/8430195.html [Accessed 2004 Aug 30]

  31. Society of Gynecologic Oncologists statement regarding OvaCheck™ [online]. Posted Feb 7, 2004. Available from URL: http://www.sgo.org/images/pdfs/policy/OvaCheck_statement.pdf [Accessed 2004 Aug 31]

  32. Department of Health & Human Services letter to Mr Levine, President and CEO Correlogic Systems, Inc. [online]. Posted Feb 18, 2004. Available from URL: http://www.fda.gov/cdrh/oivd/letters/021804-correlogic.html [Accessed 2004 Aug 31]

  33. Petricoin EF, Liotta LA. SELDI-TOF-based serum proteomic pattern diagnostics for early detection of cancer. Curr Opin Biotechnol 2004; 15: 24–30

    Article  PubMed  CAS  Google Scholar 

  34. Diamandis EP. Analysis of serum proteomic patterns for early cancer diagnosis: drawing attention to potential problems. J Natl Cancer Inst 2004; 96: 353–6

    Article  PubMed  Google Scholar 

  35. Adam BL, Qu Y, Davis JW, et al. Serum protein fingerprinting coupled with a pattern-matching algorithm distinguishes prostate cancer from benign prostate hyperplasia and healthy men. Cancer Res 2002; 62: 3609–14

    PubMed  CAS  Google Scholar 

  36. Petricoin III EF, Ornstein DK, Paweletz CP, et al. Serum proteomic patterns for detection of prostate cancer. J Natl Cancer Inst 2002; 94: 1576–8

    Article  PubMed  CAS  Google Scholar 

  37. Qu Y, Adam BL, Yasui Y, et al. Boosted decision tree analysis of surface-enhanced laser desorption/ionization mass spectral serum profiles discriminates prostate cancer from noncancer patients. Clin Chem 2002; 44: 1835–43

    Google Scholar 

Download references

Acknowledgements

This project was funded in whole or in part with Federal funds from the National Cancer Institute, National Institutes of Health, under contract no. NO1-CO-12400.

The content of this article does not necessarily reflect the views or policies of the Department of Health and Human Services nor does mention of trade names, commercial products, or organizations imply endorsement by the US government.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Timothy D. Veenstra.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Conrads, T.P., Hood, B.L., Issaq, H.J. et al. Proteomic patterns as a diagnostic tool for early-stage cancer. CNS Drugs 8, 77–85 (2004). https://doi.org/10.1007/BF03260049

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF03260049

Keywords

Navigation